Awesome GitHub RepositoriesContext Window Optimizations

Caveman is a set of tools and configurations designed for large language model token optimization. It focuses on reducing the amount of data processed during AI interactions to lower costs and maximize the available context window. The project implements a fragmented communication style that replaces full grammatical sentences with concise technical keywords. This approach extends to AI context optimization by condensing memory files and tool descriptions, and includes a specialized configuration for generating terse, one-line code reviews and short conventional commit messages. The system i

Agent-skills is a collection of structured instructions and behavioral personas designed to standardize how AI coding agents perform engineering tasks. It functions as a workflow orchestrator that maps natural language intent to repeatable technical sequences and verification checklists. The project distinguishes itself through the use of specialized markdown-defined roles, such as security auditors or test engineers, to apply targeted domain expertise. It employs an evidence-based verification model that requires runtime data or passing tests as mandatory exit criteria to ensure AI-generated

This project is a static code analysis tool and local-first code indexer that builds a persistent dependency graph of functions, classes, and imports. It functions as an AI context optimizer and codebase dependency graph, designed to reduce token usage by providing AI assistants with only the most relevant code fragments and impact analysis for a given change. The system implements a Model Context Protocol server that exposes code intelligence and architectural graph queries to external AI coding tools. It distinguishes itself by computing the change blast radius and risk scores of modificati

This project provides a system for managing agent context and session memory, featuring an agent context compactor, an AI session memory manager, and a tool output sandbox. It functions as a middleware layer and server extension for the Model Context Protocol to optimize context windows and reduce token usage. The system optimizes agent performance by sandboxing tool outputs and externalizing large data sets, replacing raw I/O with pointers and concise summaries. It employs a persistent knowledge base that indexes session history and tool outputs for retrieval via full-text search, ensuring s

PR Agent is an AI-powered code analysis tool and pull request reviewer that uses large language models to automate version control workflows. It functions as a programmatic agent that integrates with version control platforms to provide automated quality checks, explain code changes, and manage pull request documentation. The system distinguishes itself by enforcing organizational engineering standards through a customizable rule-based system. It leverages retrieval-augmented generation to inject repository context and organizational guidelines into its analysis, ensuring that feedback remain

This project is a comprehensive guide and framework for large language model prompt engineering. It provides a collection of techniques and patterns for optimizing model responses through structured system prompts, context management, and a variety of implementation patterns. The project focuses on several specialized domains, including the creation of autonomous agents through reasoning loops and the implementation of retrieval augmented generation to inject semantic context into prompts. It also provides methods for enforcing structured outputs in serialization formats like JSON or YAML for

This project is a collection of architectural templates and design patterns for building autonomous AI agents. It provides a framework for transitioning from simple prompt-response loops to goal-oriented systems that utilize structural patterns to increase autonomy and improve the reliability of complex task completion. The framework focuses on reasoning orchestration, specifically through the implementation of reflection and self-correction cycles. It enables the coordination of specialized agents via task delegation and state sharing to solve complex problems. The architectural surface cov

OpenChatKit is a training and inference toolkit for large language models. It provides a comprehensive set of tools for managing the model lifecycle, including a fine-tuning pipeline, a model weight converter, and a command-line interface for interacting with conversational agents. The toolkit features a framework for retrieval augmented generation, allowing models to incorporate relevant context from external vector indices. It also includes utilities for converting trained model checkpoints into formats compatible with standard inference libraries. The project covers conversational AI trai

Context-Engineering is a prompt engineering framework and cognitive architecture for large language models. It provides a set of patterns and methodologies for designing structured prompts and modular reasoning flows that decompose complex tasks into specialized, step-by-step problem solving templates. The project distinguishes itself through stateful prompt management and context window optimization. It maintains persistent memory across multiple interaction turns by compressing conversation history into compact internal state cells and employs techniques to maximize information density per

This project is a comprehensive framework for the orchestration, evaluation, and context management of large language model agents. It provides a set of architectural patterns and standards for designing agent interactions, integrating external tools, and establishing memory architectures to persist knowledge across sessions. The system focuses on optimizing the limited memory of language models through token-aware context compression and filesystem-based context offloading. It incorporates secure execution environments using sandboxed virtual machines and isolated containers to safely run ba

The agent-framework is an LLM agent orchestration framework and multi-agent workflow engine designed for building autonomous AI agents. It provides a tool integration layer for binding external functions, APIs, and sandboxed code as executable tools for language models. The framework distinguishes itself through a graph-based system for designing sequential and parallel task flows, featuring state management and checkpointing for long-running processes. It implements comprehensive conversational state management and an observability suite that uses telemetry to trace execution flows and monit

Open Multi-Agent is a TypeScript framework for multi-agent orchestration that decomposes natural language goals into a runtime-generated directed acyclic graph of tasks. It functions as a task orchestrator and workflow state manager, coordinating multiple AI models to execute parallel and sequential operations. The framework is distinguished by a proposer-judge consensus protocol used to validate agent outputs through a quorum of agreement. It employs provider-agnostic model routing to assign specific models to tasks based on roles or execution phases and utilizes state-based workflow checkpo

Klavis is a platform for managing Model Context Protocol (MCP) servers and providing sandboxed environments where AI agents can safely interact with external tools and services. It functions as an integration framework that orchestrates MCP server instances, exposes tools and resources for AI agents, and isolates agent interactions from production data through horizontally scalable sandbox environments. The platform distinguishes itself through its ability to generate long-horizon agentic tasks that simulate realistic tool-use workflows with live SaaS applications and production MCP servers.

Nexent es un plano de control de IA empresarial y plataforma de orquestación de agentes LLM. Proporciona un entorno sin código (zero-code) para diseñar, desplegar y gestionar agentes de IA de producción a través de un framework de colaboración multi-agente que coordina agentes autónomos especializados utilizando protocolos de mensajería estandarizados. La plataforma integra el Model Context Protocol para conectar agentes con herramientas, plugins y servicios externos mediante una interfaz de comunicación universal. Destaca además con un gestor de base de conocimientos RAG dedicado que importa documentos no estructurados y utiliza búsqueda híbrida para proporcionar contexto fundamentado para las respuestas del modelo. El sistema cubre una amplia gama de capacidades, incluyendo control de acceso basado en roles multi-inquilino, interacción multimodal a través de texto, voz e imágenes, y recuperación vectorial híbrida. También incluye un mercado para la distribución y descubrimiento de agentes, junto con herramientas de observabilidad para capturar trazas de ejecución. La plataforma soporta despliegue seguro mediante empaquetado offline contenedorizado para infraestructura aislada (air-gapped).

Este proyecto es una colección completa de materiales educativos de programación en Python, incluyendo tutoriales, ejercicios y muestras de código curadas. Sirve como un plan de estudios de aprendizaje y kit de herramientas de ingeniería de software, utilizando Jupyter Notebooks para combinar código ejecutable con texto educativo descriptivo. El repositorio proporciona guías de implementación prácticas para construir aplicaciones de modelos de lenguaje grandes, como sistemas de generación aumentada por recuperación, agentes de IA con estado y flujos de trabajo de aprendizaje automático. Se distingue por ofrecer un enfoque estructurado para flujos de trabajo de codificación agentica, cubriendo destilación de ventana de contexto, enrutamiento de modelos agnóstico al proveedor y salidas estructuradas forzadas por esquema. Los materiales cubren una amplia gama de capacidades de ingeniería de software, incluyendo programación asíncrona con colas de tareas distribuidas, desarrollo de aplicaciones web con API REST y flujos de trabajo de análisis de datos. También incluye recursos para dominar el diseño orientado a objetos, implementar tuberías de CI/CD y aplicar estándares profesionales de linting y formato.

Antigravity-kit is a multi-agent orchestrator and routing engine designed to coordinate specialized large language model agents. It functions as a conversational workflow automation tool and a context management system that executes complex tasks through a chat interface. The system utilizes a routing engine to classify user requests and dispatch them to domain-expert agents. It employs a multi-agent orchestration model that allows specialist workers to operate in parallel and combine their outputs. To manage operational efficiency, the kit includes a memory layer for storing project convent

LMOps es un framework de operaciones impulsado por la investigación para optimizar el despliegue, el ajuste fino (fine-tuning) y el rendimiento de modelos de lenguaje grandes. Proporciona un toolkit especializado para la adaptación de modelos base, aceleración de inferencia, optimización de prompts y orquestación de contexto. El framework se distingue por un acelerador de inferencia que reduce la latencia de generación de tokens verificando y copiando tramos de texto superpuestos de documentos de referencia. También cuenta con un optimizador de ingeniería de prompts que emplea aprendizaje por refuerzo, búsqueda de haz (beam search) y marcadores de lenguaje no natural para refinar automáticamente las instrucciones para una mayor calidad de salida. El toolkit cubre áreas de capacidad amplias, incluyendo el ajuste y adaptación de modelos para dominios profesionales, alineación de comportamiento utilizando retroalimentación generada por el modelo y gestión de contexto aumentada por recuperación para respuestas fundamentadas. Además, admite el escalado de aprendizaje en contexto para prompts de secuencia larga y la selección de datos de entrenamiento de alta calidad para mejorar la eficiencia del ajuste fino.

This project is an agentic development framework and autonomous software engineering system. It utilizes a coordinated network of specialized LLM agents to automate the full software development lifecycle, from codebase exploration and architectural planning to implementation and automated refactoring. The system is distinguished by an agentic memory system and a test-driven development orchestrator. It maintains project continuity across sessions by capturing architectural learnings and state in a persistent semantic database and enforces code quality through an automated cycle of generating

Acontext is an LLM orchestration backend and agent memory framework designed to manage session state and knowledge for AI agents. It functions as a context manager and orchestration layer that integrates model providers with a secure code sandbox and a zero-knowledge data store. The project is distinguished by its approach to knowledge distillation, capturing agent learnings as reusable Markdown skills and structured memory files. It provides a secure execution environment where shell commands and scripts run in isolated containers with the ability to mount these persistent skill files direct

Kimi-code is a command-line interface and orchestration framework designed to integrate autonomous AI agents into software development workflows. It functions as a terminal-based assistant that manages multi-step coding tasks, including planning, file system modifications, shell command execution, and test running, all while maintaining conversational context within a local development environment. The project distinguishes itself through a focus on secure, autonomous agent orchestration and granular control over AI interactions. It enforces strict security by requiring explicit user approval